Classification of healthy, Alzheimer’s disease, and Parkinson’s disease individuals with cortical generators of rsEEG rhythms
| Autor: | R. Lizio, Raffaele Ferri, De Lena C, Giubilei R, Maria Teresa Pascarelli, Claudio Babiloni, Fabrizio Stocchi, Laura Bonanni, Del Percio C, Giuseppe Noce, Flavio Nobili, Andrea Soricelli |
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| Rok vydání: | 2019 |
| Předmět: |
0301 basic medicine
medicine.medical_specialty Parkinson's disease Receiver operating characteristic Resting state fMRI business.industry Alpha (ethology) Disease Audiology medicine.disease 03 medical and health sciences 030104 developmental biology 0302 clinical medicine Rhythm medicine Dementia Beta (finance) business 030217 neurology & neurosurgery |
| Zdroj: | SMC |
| DOI: | 10.1109/smc.2019.8913952 |
| Popis: | In the present study, we verified the working hypothesis that cortical generators of resting state eyes-closed electroencephalographic (rsEEG) rhythms could classify with good performance (area under receiver operating characteristic, AUROC > 0.8) healthy elderly (Nold), Alzheimer’s disease (AD) and Parkinson’s disease (PD) individuals. rsEEG recordings were performed in 110 AD, 110 PD, and 75 Nold patients. Individual delta, theta, low-frequency alpha, and high-frequency bands were measured. Fixed low-frequency beta and high-frequency beta were also evaluated. eLORETA freeware measured cortical generators of rsEEG rhythms from posterior sources. Classification analysis was performed with the ROC curve. Cortical generators of posterior rsEEG rhythms at delta, theta, low-frequency alpha, high-frequency alpha, low-frequency beta, and high-frequency beta allowed a good performance (AUROC > 0.8) in the classification between healthy and neurodegenerative (i.e., AD and PD) individuals. Moreover, cortical generators of posterior rsEEG rhythms at low-frequency alpha permitted only a moderate performance (AUROC > 0.75) in the classification between AD and PD individuals. Future prospective cross-validation studies will have to test these candidate rsEEG markers for clinical applications and drug discovery. |
| Databáze: | OpenAIRE |
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